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bugfixes

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This commit is contained in:
Petrônio Cândido 2017-11-14 07:36:14 -02:00
parent 2b038968f7
commit 09e5415929
9 changed files with 69 additions and 26 deletions
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4
pyFTS/chen.py
View File

@ -71,9 +71,9 @@ class ConventionalFTS(fts.FTS):
if actual.name not in self.flrgs:
ret.append(actual.centroid)
else:
flrg = self.flrgs[actual.name]
_flrg = self.flrgs[actual.name]
ret.append(flrg.get_midpoint())
ret.append(_flrg.get_midpoint())
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])

1
pyFTS/flrg.py
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@ -20,7 +20,6 @@ class FLRG(object):
ret = self.LHS.membership(data)
return ret
def get_midpoint(self):
if self.midpoint is None:
self.midpoint = sum(self.get_midpoints())/len(self.RHS)

8
pyFTS/fts.py
View File

@ -159,15 +159,17 @@ class FTS(object):
return ndata
def doInverseTransformations(self, data, params=None, **kwargs):
ndata = data
if len(self.transformations) > 0:
if params is None:
params = [None for k in self.transformations]
for c, t in enumerate(reversed(self.transformations), start=0):
ndata = t.inverse(ndata, params[c], **kwargs)
print(c)
ndata = t.inverse(data, params[c], **kwargs)
return ndata
return ndata
else:
return data
def __str__(self):
tmp = self.name + ":\n"

2
pyFTS/hofts.py
View File

@ -27,7 +27,7 @@ class HighOrderFLRG(flrg.FLRG):
for c in self.LHS:
if len(self.strlhs) > 0:
self.strlhs += ", "
self.strlhs = self.strlhs + c.name
self.strlhs = self.strlhs + str(c)
return self.strlhs
def appendLHS(self, c):

16
pyFTS/nonstationary/honsfts.py
View File

@ -196,8 +196,8 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
if len(affected_flrgs) == 0:
tmp.append(common.check_bounds(sample[-1], self.sets, tdisp))
elif len(affected_flrgs) == 1:
if affected_flrgs[0].strLHS() in self.flrgs:
flrg = affected_flrgs[0]
flrg = affected_flrgs[0]
if flrg.strLHS() in self.flrgs:
tmp.append(self.flrgs[flrg.strLHS()].get_midpoint(tdisp))
else:
tmp.append(flrg.LHS[-1].get_midpoint(tdisp))
@ -250,13 +250,13 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
lower.append(aset.get_lower(tdisp))
upper.append(aset.get_upper(tdisp))
elif len(affected_flrgs) == 1:
if affected_flrgs[0].strLHS() in self.flrgs:
flrg = affected_flrgs[0]
lower.append(self.flrgs[flrg.strLHS()].get_lower(tdisp))
upper.append(self.flrgs[flrg.strLHS()].get_upper(tdisp))
_flrg = affected_flrgs[0]
if _flrg.strLHS() in self.flrgs:
lower.append(self.flrgs[_flrg.strLHS()].get_lower(tdisp))
upper.append(self.flrgs[_flrg.strLHS()].get_upper(tdisp))
else:
lower.append(flrg.LHS[-1].get_lower(tdisp))
upper.append(flrg.LHS[-1].get_upper(tdisp))
lower.append(_flrg.LHS[-1].get_lower(tdisp))
upper.append(_flrg.LHS[-1].get_upper(tdisp))
else:
for ct, aset in enumerate(affected_flrgs):
if aset.strLHS() in self.flrgs:

2
pyFTS/partitioners/Grid.py
View File

@ -18,7 +18,7 @@ class GridPartitioner(partitioner.Partitioner):
partlen = dlen / self.partitions
count = 0
for c in np.arange(self.min, self.max, partlen):
for c in np.linspace(self.min, self.max, self.partitions):
if self.membership_function == Membership.trimf:
sets.append(
FuzzySet.FuzzySet(self.prefix + str(count), Membership.trimf, [c - partlen, c, c + partlen],c))

1
pyFTS/song.py
View File

@ -68,7 +68,6 @@ class ConventionalFTS(fts.FTS):
ret.append( sum(mp)/len(mp))
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
return ret

43
pyFTS/tests/general.py
View File

@ -9,17 +9,18 @@ import matplotlib as plt
#from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
from pyF import Grid, Entropy, FCM, Huarng
from pyFTS.partitioners import Grid, Entropy, FCM, Huarng
from pyFTS.common import FLR,FuzzySet,Membership,Transformations, Util as cUtil
from pyFTS import fts,hofts,ifts,pwfts,tree, chen
#from pyFTS.benchmarks import benchmarks as bchmk
from pyFTS.benchmarks import naive, arima
from pyFTS.benchmarks import Measures
from numpy import random
from pyFTS.models.seasonal import SeasonalIndexer
from pyFTS.seasonal import SeasonalIndexer
os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
bc = Transformations.BoxCox(0)
diff = Transformations.Differential(1)
#ix = SeasonalIndexer.LinearSeasonalIndexer([12, 24], [720, 1],[False, False])
@ -30,8 +31,8 @@ DATASETS
#enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
#enrollments = np.array(enrollments["Enrollments"])
#passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
#passengers = np.array(passengers["Passengers"])
passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
passengers = np.array(passengers["Passengers"])
#sunspots = pd.read_csv("DataSets/sunspots.csv", sep=",")
#sunspots = np.array(sunspots["SUNACTIVITY"])
@ -63,16 +64,42 @@ DATASETS
#print(lag)
#print(a)
#'''
'''
sonda = pd.read_csv("DataSets/SONDA_BSB_15MIN_AVG.csv", sep=";")
sonda['data'] = pd.to_datetime(sonda['data'])
#sonda = sonda[:][527041:].dropna()
sonda = sonda[:][527041:].dropna()
sonda.index = np.arange(0,len(sonda.index))
sonda_treino = sonda[:105313].dropna()
sonda_teste = sonda[105314:].dropna()
'''
from pyFTS.partitioners import Grid
from pyFTS import song, chen, yu, sadaei, ismailefendi, cheng
train = passengers[:100]
test = passengers[100:]
fs = Grid.GridPartitioner(train, 10, transformation=bc)
methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, sadaei.ExponentialyWeightedFTS,
ismailefendi.ImprovedWeightedFTS, cheng.TrendWeightedFTS]
#fig, axes = plt.subplots(nrows=1, ncols=1, figsize=[15, 5])
#axes.plot(test, label="Original")
for method in methods:
model = method("")
model.appendTransformation(bc)
model.train(train, sets=fs.sets)
forecasts = model.forecast(test)
print(forecasts)
#ix_m15 = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.minute],[15],'glo_avg', name='m15')
@ -92,11 +119,11 @@ sonda_teste = sonda[105314:].dropna()
#cUtil.persist_obj(obj, "models/sonda_msfts_Entropy40_Mhm15.pkl")
ftse = cUtil.load_obj("models/sonda_ensemble_msfts.pkl")
#ftse = cUtil.load_obj("models/sonda_ensemble_msfts.pkl")
tmp = ftse.forecastDistribution(sonda_teste[850:860], h=0.5, method="gaussian")
#tmp = ftse.forecastDistribution(sonda_teste[850:860], h=0.5, method="gaussian")
print(tmp[0])
#print(tmp[0])
#'''

16
pyFTS/tests/nonstationary.py
View File

@ -4,9 +4,22 @@ from pyFTS.common import Membership, Transformations
from pyFTS.nonstationary import common,perturbation, partitioners, util,nsfts, honsfts, cvfts
from pyFTS.partitioners import Grid
import matplotlib.pyplot as plt
from pyFTS.common import Util as cUtil
import pandas as pd
os.chdir("/home/petronio/Dropbox/Doutorado/Codigos/")
data = pd.read_csv("DataSets/synthetic_nonstationary_dataset_A.csv", sep=";")
data = np.array(data["0"][:])
for ct, train, test in cUtil.sliding_window(data, 300):
for partition in np.arange(10,50):
print(partition)
tmp_fsp = Grid.GridPartitioner(train, partition)
print(len(tmp_fsp.sets))
fsp = partitioners.PolynomialNonStationaryPartitioner(train, tmp_fsp, window_size=35, degree=1)
'''
diff = Transformations.Differential(1)
def generate_heteroskedastic_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=10, num=35):
@ -43,6 +56,7 @@ fs = partitioners.ConstantNonStationaryPartitioner(train, tmp_fs,
width=perturbation.polynomial,
width_params=[1,0],
width_roots=0)
'''
"""
perturb = [0.5, 0.25]
for i in [0,1]:
@ -51,6 +65,7 @@ for i in [0,1]:
for i in [0,1]:
print(fs.sets[i].perturbated_parameters[perturb[i]])
"""
'''
#nsfts1 = nsfts.NonStationaryFTS("", partitioner=fs)
nsfts1 = cvfts.ConditionalVarianceFTS("", partitioner=fs)
@ -114,3 +129,4 @@ axes.plot(testp, label="Original")
handles0, labels0 = axes.get_legend_handles_labels()
lgd = axes.legend(handles0, labels0, loc=2)
"""
'''